code: def demo_sent_subjectivity(text):
      """
      Classify a single sentence as subjective or objective using a stored
      SentimentAnalyzer.
  
      :param text: a sentence whose subjectivity has to be classified.
      """
      from nltk.classify import NaiveBayesClassifier
      from nltk.tokenize import regexp
  
      word_tokenizer = regexp.WhitespaceTokenizer()
      try:
          sentim_analyzer = load("sa_subjectivity.pickle")
      except LookupError:
          print("Cannot find the sentiment analyzer you want to load.")
          print("Training a new one using NaiveBayesClassifier.")
          sentim_analyzer = demo_subjectivity(
              NaiveBayesClassifier.train, True
          )
  
      # Tokenize and convert to lower case
      tokenized_text = [
          word.lower() for word in word_tokenizer.tokenize(text)
      ]
      print(sentim_analyzer.classify(tokenized_text))
  
imports: import codecs
  import csv
  import json
  import pickle
  import random
  import re
  import sys
  import time
  from copy import deepcopy
  import nltk
  from nltk.corpus import CategorizedPlaintextCorpusReader
  from nltk.data import load
  from nltk.tokenize.casual import EMOTICON_RE
label_desc: Use image node_id 13 for calling a global function with example usage: demo_sent_subjectivity(text) without return types